Bioelectrochemical Sensing Using Benchtop Fabricated Nanoroughened Microstructured Electrodes
Bibliographic record
Abstract
Abstract Cost‐effective miniaturized electrodes that maintain a high electroactive surface area (ESA) are needed for the widespread deployment of point‐of‐care sensors. Cost‐effective methods are recently developed to fabricate nanoroughened microstructured gold electrodes (NR‐MSEs) with ultrahigh ESA. In this work, the effectiveness of NR‐MSEs for bioelectrochemical enzymatic sensors is evaluated. A glucose sensor is constructed by first casting onto NR‐MSEs a solution containing reduced graphene oxide decorated with gold nanoparticles, glucose oxidase, and glutaraldehyde, followed by a solution containing ferrocene, and a layer of chitosan to prevent the leakage of sensor components. A urea biosensor is also fabricated using Nafion as a cationic exchanger for the electropolymerization of polyaniline, followed by the deposition of a composite containing urease, bovine serum albumin, and glutaraldehyde. The limit of quantification for both biosensors is below clinically relevant concentrations of the analytes in biofluids, 0.67 m m for glucose and 1.70 m m for urea. The sensors exhibit excellent performance in complex matrixes (human blood serum and wine for glucose and human blood serum and urine for urea), with recovery for spiked analytes in the range of 92–108%. It is anticipated that NR‐MSEs will expedite the development of highly sensitive bioelectrochemical sensors for use in resource‐limited settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".